|Table of Contents|

The diagnostic value of S-Detect technology combined with clinical characteristics based on BI-RADS classification for benign and malignant breast masses

Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:
2025 04
Page:
607-613
Research Field:
Publishing date:

Info

Title:
The diagnostic value of S-Detect technology combined with clinical characteristics based on BI-RADS classification for benign and malignant breast masses
Author(s):
LI Wenxiao; ZHANG Yurui; LIU Wen; YAN Xiaojun; SHI Linan; CAO Chunli; WANG Sirui; LI Jun; CHENG Jing
Department of Ultrasound,the First Affiliated Hospital of Shihezi University,Xinjiang Shihezi 832008,China.
Keywords:
ultrasound; breast mass; S-Detect technology; clinical characteristics; diagnostic efficiency
PACS:
R737.9
DOI:
10.3969/j.issn.1672-4992.2025.04.009
Abstract:
Objective:To evaluate the diagnostic efficacy of S-Detect technology in four different sections of breast mass,and to establish a diagnostic model of breast mass combined with the classification of the Breast Imaging Reporting and Data System (BI-RADS) and clinical characteristics.Methods:The results of routine ultrasound,clinical features and S-Detect were prospectively collected in 120 lumps.They were divided into benign group and malignant group according to postoperative pathological results.ROC curves of four different sections of the tumor were constructed and their diagnostic efficiency was compared.The BI-RADS classification was redefined by the results of S-Detect identification of the four sections of the mass.Through univariate and multivariate Logistic regression analysis,the risk factors of breast cancer were screened.The diagnostic model was established and the consistency test was carried out.Results:Among the 120 lumps,45 lumps were malignant and 75 lumps were benign.The sensitivity of S-Detect in the diagnosis of breast cancer in horizontal transverse section,vertical longitudinal section,maximum long axis section and longest axis transverse section was 76.923%,63.385%,80.769% and 80.769%,respectively.The specificity was 89.655%,91.379%,96.552%,89.655%.The positive predictive value was 76.923%,77.273%,91.304%,77.778%,and the negative predictive value was 89.655%,88.484%,91.803%,91.228%,respectively.The accuracy rates were 85.714%,83.333%,91.667% and 86.904%,respectively.The area under ROC curve (AUC) was 0.824,0.792,0.887 and 0.852,respectively.The results of univariate analysis showed that the difference between the two groups was statistically significant in whether the tumor was touched,clinical symptoms,menopause,left and right diameter,anterior-to-posterior diameter,original BI-RADS,and adjusted BI-RADS.The results of multivariate analysis showed that the left and right diameter greater than 1.75 cm,menopause,original BI-RADS classification 4a and above,and adjusted BI-RADS classification 4a and above were independent risk factors for breast cancer (P<0.05),and their AUC was 0.867,0.676,0.779,0.891,respectively.The diagnostic model was established by combining the above characteristics.The AUC of the diagnostic model was 0.978,which was greater than all single feature parameters.The consistency test results showed that the diagnostic model had good diagnostic efficiency (Kappa value=0.719).Conclusion:Among the four different sections of breast mass,the longest axis transverse section S-Detect has better diagnostic efficiency.Artificial intelligence S-Detect technology combined with clinical characteristics is of great significance in the differential diagnosis of benign and malignant breast masses.

References:

［1］ BRAY F,LAVERSANNE M,SUNG H,et al.Global cancer statistics 2022:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries［J］.CA Cancer J Clin,2024,74(3):229-263.
［2］ HAN M,HA EJ,PARK JH.Computer-aided diagnostic system for thyroid nodules on ultrasonography:diagnostic performance based on the thyroid imaging reporting and data system classification and dichotomous outcomes［J］.AJNR Am J Neuroradiol,2021,42(3):559-565.
［3］ ZHAO C,XIAO M,MA L,et al.Enhancing performance of breast ultrasound in opportunistic screening women by a deep learning-based system:a multicenter prospective study［J］.Front Oncol,2022,12:804632.
［4］ 曹力,张会萍,周毓青,等.不同年资超声科医师应用S-Detect技术诊断乳腺结节的价值研究［J］.肿瘤影像学,2022,31(04):439-443.CAO L,ZHANG HP,ZHOU YQ,et al.Value of S-Detect technique for diagnosing breast tumors by well-experienced and less-experienced ultrasound physicians［J］.Oncoradiology,2022,31(04):439-443.
［5］ WEI Q,ZENG SE,WANG LP,et al.The added value of a computer-aided diagnosis system in differential diagnosis of breast lesions by radiologists with different experience［J］.J Ultrasound Med,2022,41(6):1355-1363.
［6］ SHEN YT,CHEN L,YUE WW,et al.Artificial intelligence in ultrasound［J］.Eur J Radiol,2021,139:109717.
［7］ JANU E,KRIKAVOVA L,LITTLE J,et al.Prospective evaluation of contrast-enhanced ultrasound of breast BI-RADS 3-5 lesions［J］.BMC Med Imaging,2020,20(1):66.
［8］ LIANG YP,ZHOU P,ZHANG J,et al.Performance of computer-aided diagnosis in ultrasonography for detection of breast lesions less and more than 2 cm:prospective comparative study［J］.JMIR Med Inform,2020,8(3):e16334.
［9］ 刘琰,王雁,丁全全,等.超声S-Detect~(TM)技术在鉴别乳腺良恶性肿块中的应用分析［J］.中国超声医学杂志,2020,36(01):33-36.LIU Y,WANG Y,DING QQ,et al.Application of ultrasonography in differentiating benign and malignant breast masses［J］.Chinese J Ultrasound Med,2020,36(01):33-36.
［10］ ZHU Y,ZHAN W,JIA X,et al.Clinical application of computer-aided diagnosis for breast ultrasonography:Factors that lead to discordant results in radial and antiradial planes［J］.Cancer Manag Res,2022,14:751-760.
［11］ 潘加珍,查海玲,杜丽雯,等.S-Detect技术与常规超声不同联合方案在乳腺结节评估中的应用［J］.南京医科大学学报(自然科学版),2021,41(02):262-267.PAN JZ,ZHA HL,DU LW,et al.The application in evaluation of breast nodules by different combined schemes of S-Detect technique and conventional ultrasound［J］.Journal of Nanjing Medical University(Natural Sciences),2021,41(02):262-267.
［12］ LIANG YP,ZHANG J,ZHOU P,et al.Evaluation of the quadri-planes method in computer-aided diagnosis of breast lesions by ultrasonography:prospective single-center study［J］.JMIR Med Inform,2020,8(5):e18251.
［13］ 中国抗癌协会乳腺癌专业委员会.中国抗癌协会乳腺癌诊治指南与规范（2021年版）［J］.中国癌症杂志,2021,31(10):954-1040.Breast cancer Professional Committee of China Anti Cancer Association.Guidelines and specifications for breast cancer diagnosis and treatment of China Anti Cancer Association (2021)［J］.China Oncology,2021,31(10):954-1040.
［14］ 任文辉,闫慧姣,党乐,等.2016-2020年鄂尔多斯市35～64岁适龄女性乳腺癌筛查现状分析［J］.中国预防医学杂志,2022,23(09):698-704.REN WH,YAN HJ,DANG L,et al.Breast cancer screening in women aged 35~64 years in Ordos in 2016—2020［J］.Chin Prev Med,2022,23(09):698-704.
［15］ 吴迪,高茜,高超,等.基于乳腺磁共振背景实质强化程度与乳腺癌不同分子亚型及临床因素的相关性分析［J］.磁共振成像,2022,13(05):40-44.WU D,GAO Q,GAO C,et al.Correlation analysis of breast MRI-based background parenchymal enhancement with different molecular subtypes and clinical factors of breast cancer［J］.Chin J Magn Reson Imag,2022,13(05):40-44.

Memo

Memo:
National Natural Science Foundation of China(No.82060318，82460353，81860498，81560433);国家自然科学基金（编号：82060318，82460353，81860498，81560433）；天山英才科技创新团队：中亚地区高发疾病防治应用研究创新团队（编号：2023TSYCTD0020）；兵团科技计划项目（编号：2022CB002-04）；石河子大学自然科学基金（编号：ZZZC2023035，ZZZC2023040，ZZZC201955A）；石河子大学第一附属医院青年基金项目（编号：QN202126，QN202107）
Last Update: 1900-01-01